Overview

Dataset statistics

Number of variables6
Number of observations280
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory14.3 KiB
Average record size in memory52.5 B

Variable types

Text1
Numeric4
Categorical1

Dataset

Description경상남도 거창군 행정마을별 데이터로 마을명, 세대수, 인구(전체), 인구(남), 인구(여) 등의 항목을 제공합니다.
Author경상남도 거창군
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=3035560

Alerts

데이터기준일 has constant value ""Constant
세대수 is highly overall correlated with 인구(전체) and 2 other fieldsHigh correlation
인구(전체) is highly overall correlated with 세대수 and 2 other fieldsHigh correlation
인구(남) is highly overall correlated with 세대수 and 2 other fieldsHigh correlation
인구(여) is highly overall correlated with 세대수 and 2 other fieldsHigh correlation

Reproduction

Analysis started2024-04-17 18:10:55.857658
Analysis finished2024-04-17 18:10:57.307697
Duration1.45 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

마을명
Text

Distinct262
Distinct (%)93.6%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
2024-04-18T03:10:57.584961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length2
Mean length2.2178571
Min length2

Characters and Unicode

Total characters621
Distinct characters170
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique247 ?
Unique (%)88.2%

Sample

1st row거창군
2nd row거창읍
3rd row원상동
4th row상동
5th row하동
ValueCountFrequency (%)
신기 4
 
1.3%
창촌 3
 
1.0%
당동 2
 
0.7%
촌 2
 
0.7%
암 2
 
0.7%
용 2
 
0.7%
산포 2
 
0.7%
학동 2
 
0.7%
신촌 2
 
0.7%
월포 2
 
0.7%
Other values (267) 276
92.3%
2024-04-18T03:10:58.008028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 41
 
6.6%
산 28
 
4.5%
19
 
3.1%
촌 15
 
2.4%
원 15
 
2.4%
대 13
 
2.1%
기 13
 
2.1%
곡 12
 
1.9%
상 12
 
1.9%
면 12
 
1.9%
Other values (160) 441
71.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 596
96.0%
Space Separator 19
 
3.1%
Decimal Number 6
 
1.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 41
 
6.9%
산 28
 
4.7%
촌 15
 
2.5%
원 15
 
2.5%
대 13
 
2.2%
기 13
 
2.2%
곡 12
 
2.0%
상 12
 
2.0%
면 12
 
2.0%
신 10
 
1.7%
Other values (157) 425
71.3%
Decimal Number
ValueCountFrequency (%)
2 3
50.0%
1 3
50.0%
Space Separator
ValueCountFrequency (%)
19
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 596
96.0%
Common 25
 
4.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 41
 
6.9%
산 28
 
4.7%
촌 15
 
2.5%
원 15
 
2.5%
대 13
 
2.2%
기 13
 
2.2%
곡 12
 
2.0%
상 12
 
2.0%
면 12
 
2.0%
신 10
 
1.7%
Other values (157) 425
71.3%
Common
ValueCountFrequency (%)
19
76.0%
2 3
 
12.0%
1 3
 
12.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 596
96.0%
ASCII 25
 
4.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 41
 
6.9%
산 28
 
4.7%
촌 15
 
2.5%
원 15
 
2.5%
대 13
 
2.2%
기 13
 
2.2%
곡 12
 
2.0%
상 12
 
2.0%
면 12
 
2.0%
신 10
 
1.7%
Other values (157) 425
71.3%
ASCII
ValueCountFrequency (%)
19
76.0%
2 3
 
12.0%
1 3
 
12.0%

세대수
Real number (ℝ)

HIGH CORRELATION 

Distinct121
Distinct (%)43.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean329.13214
Minimum11
Maximum30719
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 KiB
2024-04-18T03:10:58.332979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile21
Q136.75
median49
Q377.5
95-th percentile935.35
Maximum30719
Range30708
Interquartile range (IQR)40.75

Descriptive statistics

Standard deviation2162.5688
Coefficient of variation (CV)6.5705185
Kurtosis158.45333
Mean329.13214
Median Absolute Deviation (MAD)17
Skewness12.211472
Sum92157
Variance4676704
MonotonicityNot monotonic
2024-04-18T03:10:58.440635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
45 9
 
3.2%
28 8
 
2.9%
36 8
 
2.9%
43 8
 
2.9%
41 7
 
2.5%
42 7
 
2.5%
48 7
 
2.5%
54 7
 
2.5%
49 6
 
2.1%
53 6
 
2.1%
Other values (111) 207
73.9%
ValueCountFrequency (%)
11 1
 
0.4%
13 1
 
0.4%
15 2
0.7%
16 1
 
0.4%
17 3
1.1%
18 2
0.7%
19 1
 
0.4%
21 4
1.4%
24 3
1.1%
25 3
1.1%
ValueCountFrequency (%)
30719 1
0.4%
18598 1
0.4%
3379 1
0.4%
2127 1
0.4%
2102 1
0.4%
1844 1
0.4%
1678 1
0.4%
1637 1
0.4%
1339 1
0.4%
1337 1
0.4%

인구(전체)
Real number (ℝ)

HIGH CORRELATION 

Distinct153
Distinct (%)54.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean646.32857
Minimum19
Maximum60324
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 KiB
2024-04-18T03:10:58.548406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19
5-th percentile36.85
Q162
median82.5
Q3132.5
95-th percentile1795.45
Maximum60324
Range60305
Interquartile range (IQR)70.5

Descriptive statistics

Standard deviation4362.31
Coefficient of variation (CV)6.7493689
Kurtosis149.47767
Mean646.32857
Median Absolute Deviation (MAD)27.5
Skewness11.911378
Sum180972
Variance19029748
MonotonicityNot monotonic
2024-04-18T03:10:58.650226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
66 7
 
2.5%
71 6
 
2.1%
70 6
 
2.1%
74 5
 
1.8%
79 5
 
1.8%
42 5
 
1.8%
77 5
 
1.8%
106 5
 
1.8%
60 5
 
1.8%
59 5
 
1.8%
Other values (143) 226
80.7%
ValueCountFrequency (%)
19 1
 
0.4%
21 1
 
0.4%
22 1
 
0.4%
23 1
 
0.4%
26 1
 
0.4%
27 3
1.1%
29 3
1.1%
30 2
0.7%
34 1
 
0.4%
37 1
 
0.4%
ValueCountFrequency (%)
60324 1
0.4%
39982 1
0.4%
7699 1
0.4%
4485 1
0.4%
3954 1
0.4%
3922 1
0.4%
3553 1
0.4%
3147 1
0.4%
2746 1
0.4%
2295 1
0.4%

인구(남)
Real number (ℝ)

HIGH CORRELATION 

Distinct115
Distinct (%)41.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean317.20714
Minimum10
Maximum29606
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 KiB
2024-04-18T03:10:58.751821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile17
Q130
median40
Q366
95-th percentile857.05
Maximum29606
Range29596
Interquartile range (IQR)36

Descriptive statistics

Standard deviation2142.6325
Coefficient of variation (CV)6.7546792
Kurtosis149.24368
Mean317.20714
Median Absolute Deviation (MAD)13.5
Skewness11.903828
Sum88818
Variance4590874
MonotonicityNot monotonic
2024-04-18T03:10:58.853073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
41 10
 
3.6%
40 9
 
3.2%
30 9
 
3.2%
37 8
 
2.9%
33 8
 
2.9%
39 8
 
2.9%
34 7
 
2.5%
35 7
 
2.5%
23 7
 
2.5%
38 7
 
2.5%
Other values (105) 200
71.4%
ValueCountFrequency (%)
10 1
 
0.4%
11 1
 
0.4%
12 2
0.7%
13 3
1.1%
14 2
0.7%
15 2
0.7%
16 2
0.7%
17 4
1.4%
18 2
0.7%
19 3
1.1%
ValueCountFrequency (%)
29606 1
0.4%
19671 1
0.4%
3836 1
0.4%
2240 1
0.4%
1900 1
0.4%
1871 1
0.4%
1721 1
0.4%
1520 1
0.4%
1304 1
0.4%
1163 1
0.4%

인구(여)
Real number (ℝ)

HIGH CORRELATION 

Distinct116
Distinct (%)41.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean329.12143
Minimum6
Maximum30718
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 KiB
2024-04-18T03:10:58.954907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile17.9
Q131
median42
Q369.25
95-th percentile909.6
Maximum30718
Range30712
Interquartile range (IQR)38.25

Descriptive statistics

Standard deviation2219.7276
Coefficient of variation (CV)6.7444031
Kurtosis149.6916
Mean329.12143
Median Absolute Deviation (MAD)14
Skewness11.918006
Sum92154
Variance4927190.5
MonotonicityNot monotonic
2024-04-18T03:10:59.079254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31 13
 
4.6%
38 9
 
3.2%
36 9
 
3.2%
32 8
 
2.9%
41 8
 
2.9%
39 7
 
2.5%
45 7
 
2.5%
24 7
 
2.5%
56 6
 
2.1%
33 6
 
2.1%
Other values (106) 200
71.4%
ValueCountFrequency (%)
6 1
 
0.4%
7 1
 
0.4%
10 2
0.7%
11 2
0.7%
13 3
1.1%
14 2
0.7%
15 1
 
0.4%
16 2
0.7%
18 1
 
0.4%
19 3
1.1%
ValueCountFrequency (%)
30718 1
0.4%
20311 1
0.4%
3863 1
0.4%
2245 1
0.4%
2083 1
0.4%
2022 1
0.4%
1832 1
0.4%
1627 1
0.4%
1442 1
0.4%
1179 1
0.4%

데이터기준일
Categorical

CONSTANT 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
2023-02-28
280 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-02-28
2nd row2023-02-28
3rd row2023-02-28
4th row2023-02-28
5th row2023-02-28

Common Values

ValueCountFrequency (%)
2023-02-28 280
100.0%

Length

2024-04-18T03:10:59.176259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T03:10:59.243347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-02-28 280
100.0%

Interactions

2024-04-18T03:10:56.855047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:10:56.024932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:10:56.292832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:10:56.583098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:10:56.936421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:10:56.094943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:10:56.371049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:10:56.645336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:10:57.020476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:10:56.160470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:10:56.449795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:10:56.709148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:10:57.093145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:10:56.226127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:10:56.516574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T03:10:56.772647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-18T03:10:59.286171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세대수인구(전체)인구(남)인구(여)
세대수1.0001.0001.0001.000
인구(전체)1.0001.0001.0001.000
인구(남)1.0001.0001.0001.000
인구(여)1.0001.0001.0001.000
2024-04-18T03:10:59.353900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세대수인구(전체)인구(남)인구(여)
세대수1.0000.9820.9660.968
인구(전체)0.9821.0000.9840.986
인구(남)0.9660.9841.0000.943
인구(여)0.9680.9860.9431.000

Missing values

2024-04-18T03:10:57.186649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-18T03:10:57.273876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

마을명세대수인구(전체)인구(남)인구(여)데이터기준일
0거창군307196032429606307182023-02-28
1거창읍185983998219671203112023-02-28
2원상동78917958559402023-02-28
3상동33797699383638632023-02-28
4하동63911535545992023-02-28
5죽전13392746130414422023-02-28
6동동18443954187120832023-02-28
7강양72115547717832023-02-28
8개봉16783922190020222023-02-28
9동산244623232023-02-28
마을명세대수인구(전체)인구(남)인구(여)데이터기준일
270송 정447337362023-02-28
271용 암264223192023-02-28
272개 금649849492023-02-28
273심 방547939402023-02-28
274중 촌548748392023-02-28
275회 남274422222023-02-28
276추 동457939402023-02-28
277해 평284415292023-02-28
278용 산115192921002023-02-28
279율 리243418162023-02-28